{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4485f20b",
   "metadata": {},
   "source": [
    "# DAY 37\n",
    "\n",
    "我今天的笔记是用cpu训练的，请自行修改为gpu训练\n",
    "\n",
    "仍然是循序渐进，先复习之前的代码\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2dbd387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练进度: 100%|██████████| 20000/20000 [00:06<00:00, 3252.53epoch/s, Loss=0.0618]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 6.15 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率: 96.67%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm  # 导入tqdm库用于进度条显示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # 忽略警告信息\n",
    "\n",
    "# 设置GPU设备\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 加载鸢尾花数据集\n",
    "iris = load_iris()\n",
    "X = iris.data  # 特征数据\n",
    "y = iris.target  # 标签数据\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 归一化数据\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 将数据转换为PyTorch张量并移至GPU\n",
    "X_train = torch.FloatTensor(X_train).to(device)\n",
    "y_train = torch.LongTensor(y_train).to(device)\n",
    "X_test = torch.FloatTensor(X_test).to(device)\n",
    "y_test = torch.LongTensor(y_test).to(device)\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(4, 10)  # 输入层到隐藏层\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(10, 3)  # 隐藏层到输出层\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 实例化模型并移至GPU\n",
    "model = MLP().to(device)\n",
    "\n",
    "# 分类问题使用交叉熵损失函数\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# 使用随机梯度下降优化器\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 20000  # 训练的轮数\n",
    "\n",
    "# 用于存储每100个epoch的损失值和对应的epoch数\n",
    "losses = []\n",
    "epochs = []\n",
    "\n",
    "start_time = time.time()  # 记录开始时间\n",
    "\n",
    "# 创建tqdm进度条\n",
    "with tqdm(total=num_epochs, desc=\"训练进度\", unit=\"epoch\") as pbar:\n",
    "    # 训练模型\n",
    "    for epoch in range(num_epochs):\n",
    "        # 前向传播\n",
    "        outputs = model(X_train)  # 隐式调用forward函数\n",
    "        loss = criterion(outputs, y_train)\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # 记录损失值并更新进度条\n",
    "        if (epoch + 1) % 200 == 0:\n",
    "            losses.append(loss.item())\n",
    "            epochs.append(epoch + 1)\n",
    "            # 更新进度条的描述信息\n",
    "            pbar.set_postfix({'Loss': f'{loss.item():.4f}'})\n",
    "\n",
    "        # 每1000个epoch更新一次进度条\n",
    "        if (epoch + 1) % 1000 == 0:\n",
    "            pbar.update(1000)  # 更新进度条\n",
    "\n",
    "    # 确保进度条达到100%\n",
    "    if pbar.n < num_epochs:\n",
    "        pbar.update(num_epochs - pbar.n)  # 计算剩余的进度并更新\n",
    "\n",
    "time_all = time.time() - start_time  # 计算训练时间\n",
    "print(f'Training time: {time_all:.2f} seconds')\n",
    "\n",
    "# 可视化损失曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss over Epochs')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 在测试集上评估模型，此时model内部已经是训练好的参数了\n",
    "# 评估模型\n",
    "model.eval() # 设置模型为评估模式\n",
    "with torch.no_grad(): # torch.no_grad()的作用是禁用梯度计算，可以提高模型推理速度\n",
    "    outputs = model(X_test)  # 对测试数据进行前向传播，获得预测结果\n",
    "    _, predicted = torch.max(outputs, 1) # torch.max(outputs, 1)返回每行的最大值和对应的索引\n",
    "    #这个函数返回2个值，分别是最大值和对应索引，参数1是在第1维度（行）上找最大值，_ 是Python的约定，表示忽略这个返回值，所以这个写法是找到每一行最大值的下标\n",
    "    # 此时outputs是一个tensor，p每一行是一个样本，每一行有3个值，分别是属于3个类别的概率，取最大值的下标就是预测的类别\n",
    "\n",
    "\n",
    "    # predicted == y_test判断预测值和真实值是否相等，返回一个tensor，1表示相等，0表示不等，然后求和，再除以y_test.size(0)得到准确率\n",
    "    # 因为这个时候数据是tensor，所以需要用item()方法将tensor转化为Python的标量\n",
    "    # 之所以不用sklearn的accuracy_score函数，是因为这个函数是在CPU上运行的，需要将数据转移到CPU上，这样会慢一些\n",
    "    # size(0)获取第0维的长度，即样本数量\n",
    "\n",
    "    correct = (predicted == y_test).sum().item() # 计算预测正确的样本数\n",
    "    accuracy = correct / y_test.size(0)\n",
    "    print(f'测试集准确率: {accuracy * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3423d79",
   "metadata": {},
   "source": [
    "训练集的loss在下降，但是有可能出现过拟合现象：模型过度学习了训练集的信息，导致在测试集上表现不理想。\n",
    "\n",
    "所以很自然的，我们想同步打印测试集的loss，以判断是否出现过拟合现象。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57aa07d",
   "metadata": {},
   "source": [
    "### 过拟合的判断"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bec828c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练进度: 100%|██████████| 20000/20000 [00:06<00:00, 3245.61epoch/s, Train Loss=0.0633, Test Loss=0.0578]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 6.16 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率: 96.67%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm  # 导入tqdm库用于进度条显示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # 忽略警告信息\n",
    "\n",
    "# 设置GPU设备\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 加载鸢尾花数据集\n",
    "iris = load_iris()\n",
    "X = iris.data  # 特征数据\n",
    "y = iris.target  # 标签数据\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 归一化数据\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 将数据转换为PyTorch张量并移至GPU\n",
    "X_train = torch.FloatTensor(X_train).to(device)\n",
    "y_train = torch.LongTensor(y_train).to(device)\n",
    "X_test = torch.FloatTensor(X_test).to(device)\n",
    "y_test = torch.LongTensor(y_test).to(device)\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(4, 10)  # 输入层到隐藏层\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(10, 3)  # 隐藏层到输出层\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 实例化模型并移至GPU\n",
    "model = MLP().to(device)\n",
    "\n",
    "# 分类问题使用交叉熵损失函数\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# 使用随机梯度下降优化器\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 20000  # 训练的轮数\n",
    "\n",
    "# 用于存储每200个epoch的损失值和对应的epoch数\n",
    "train_losses = [] # 存储训练集损失\n",
    "test_losses = [] # 新增：存储测试集损失\n",
    "epochs = []\n",
    "\n",
    "start_time = time.time()  # 记录开始时间\n",
    "\n",
    "# 创建tqdm进度条\n",
    "with tqdm(total=num_epochs, desc=\"训练进度\", unit=\"epoch\") as pbar:\n",
    "    # 训练模型\n",
    "    for epoch in range(num_epochs):\n",
    "        # 前向传播\n",
    "        outputs = model(X_train)  # 隐式调用forward函数\n",
    "        train_loss = criterion(outputs, y_train)\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        train_loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # 记录损失值并更新进度条\n",
    "        if (epoch + 1) % 200 == 0:\n",
    "            # 计算测试集损失，新增代码\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                test_outputs = model(X_test)\n",
    "                test_loss = criterion(test_outputs, y_test)\n",
    "            model.train()\n",
    "            \n",
    "            train_losses.append(train_loss.item())\n",
    "            test_losses.append(test_loss.item())\n",
    "            epochs.append(epoch + 1)\n",
    "            \n",
    "            # 更新进度条的描述信息\n",
    "            pbar.set_postfix({'Train Loss': f'{train_loss.item():.4f}', 'Test Loss': f'{test_loss.item():.4f}'})\n",
    "\n",
    "        # 每1000个epoch更新一次进度条\n",
    "        if (epoch + 1) % 1000 == 0:\n",
    "            pbar.update(1000)  # 更新进度条\n",
    "\n",
    "    # 确保进度条达到100%\n",
    "    if pbar.n < num_epochs:\n",
    "        pbar.update(num_epochs - pbar.n)  # 计算剩余的进度并更新\n",
    "\n",
    "time_all = time.time() - start_time  # 计算训练时间\n",
    "print(f'Training time: {time_all:.2f} seconds')\n",
    "\n",
    "# 可视化损失曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, train_losses, label='Train Loss') # 原始代码已有\n",
    "plt.plot(epochs, test_losses, label='Test Loss')  # 新增：测试集损失曲线\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss over Epochs')\n",
    "plt.legend() # 新增：显示图例\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 在测试集上评估模型，此时model内部已经是训练好的参数了\n",
    "# 评估模型\n",
    "model.eval() # 设置模型为评估模式\n",
    "with torch.no_grad(): # torch.no_grad()的作用是禁用梯度计算，可以提高模型推理速度\n",
    "    outputs = model(X_test)  # 对测试数据进行前向传播，获得预测结果\n",
    "    _, predicted = torch.max(outputs, 1) # torch.max(outputs, 1)返回每行的最大值和对应的索引\n",
    "    correct = (predicted == y_test).sum().item() # 计算预测正确的样本数\n",
    "    accuracy = correct / y_test.size(0)\n",
    "    print(f'测试集准确率: {accuracy * 100:.2f}%')    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51399a3b",
   "metadata": {},
   "source": [
    "实际上，打印测试集的loss和同步打印测试集的评估指标，是一个逻辑，但是打印loss可以体现在一个图中。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae9ebae",
   "metadata": {},
   "source": [
    "### 模型的保存和加载"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b518b8c0",
   "metadata": {},
   "source": [
    "深度学习中模型的保存与加载主要涉及参数（权重）和整个模型结构的存储，同时需兼顾训练状态（如优化器参数、轮次等）以支持断点续训。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e4abdb",
   "metadata": {},
   "source": [
    "#### 仅保存模型参数（推荐）\n",
    "\n",
    "- 原理：保存模型的权重参数，不保存模型结构代码。加载时需提前定义与训练时一致的模型类。\n",
    "- 优点：文件体积小（仅含参数），跨框架兼容性强（需自行定义模型结构）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd4a0ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型参数\n",
    "torch.save(model.state_dict(), \"model_weights.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a797cf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载参数（需先定义模型结构）\n",
    "model = MLP()  # 初始化与训练时相同的模型结构\n",
    "model.load_state_dict(torch.load(\"model_weights.pth\"))\n",
    "# model.eval()  # 切换至推理模式（可选）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b730c1",
   "metadata": {},
   "source": [
    "#### 保存模型+权重\n",
    "- 原理：保存模型结构及参数\n",
    "- 优点：加载时无需提前定义模型类\n",
    "- 缺点：文件体积大，依赖训练时的代码环境（如自定义层可能报错）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "23b791a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLP(\n",
       "  (fc1): Linear(in_features=4, out_features=10, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=10, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存整个模型\n",
    "torch.save(model, \"full_model.pth\")\n",
    "\n",
    "# 加载模型（无需提前定义类，但需确保环境一致）\n",
    "model = torch.load(\"full_model.pth\")\n",
    "model.eval()  # 切换至推理模式（可选）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9366e8",
   "metadata": {},
   "source": [
    "#### 保存训练状态（断点续训）\n",
    "- 原理：保存模型参数、优化器状态（学习率、动量）、训练轮次、损失值等完整训练状态，用于中断后继续训练。\n",
    "- 适用场景：长时间训练任务（如分布式训练、算力中断）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b373022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 保存训练状态\n",
    "# checkpoint = {\n",
    "#     \"model_state_dict\": model.state_dict(),\n",
    "#     \"optimizer_state_dict\": optimizer.state_dict(),\n",
    "#     \"epoch\": epoch,\n",
    "#     \"loss\": best_loss,\n",
    "# }\n",
    "# torch.save(checkpoint, \"checkpoint.pth\")\n",
    "\n",
    "# # 加载并续训\n",
    "# model = MLP()\n",
    "# optimizer = torch.optim.Adam(model.parameters())\n",
    "# checkpoint = torch.load(\"checkpoint.pth\")\n",
    "\n",
    "# model.load_state_dict(checkpoint[\"model_state_dict\"])\n",
    "# optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n",
    "# start_epoch = checkpoint[\"epoch\"] + 1  # 从下一轮开始训练\n",
    "# best_loss = checkpoint[\"loss\"]\n",
    "\n",
    "# # 继续训练循环\n",
    "# for epoch in range(start_epoch, num_epochs):\n",
    "#     train(model, optimizer, ...)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "0007e7e7",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ed2aa3",
   "metadata": {},
   "source": [
    "### 早停法(early stop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f450e1",
   "metadata": {},
   "source": [
    "我们梳理下过拟合的情况\n",
    "\n",
    "- 正常情况：训练集和测试集损失同步下降，最终趋于稳定。\n",
    "\n",
    "- 过拟合：训练集损失持续下降，但测试集损失在某一时刻开始上升（或不再下降）。\n",
    "\n",
    "如果可以监控验证集的指标不再变好，此时提前终止训练，避免模型对训练集过度拟合。----监控的对象是验证集的指标。这种策略叫早停法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9c7427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练进度: 100%|██████████| 20000/20000 [00:05<00:00, 3387.31epoch/s, Train Loss=0.0607, Test Loss=0.0529]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 5.91 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率: 96.67%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm  # 导入tqdm库用于进度条显示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # 忽略警告信息\n",
    "\n",
    "# 设置GPU设备\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 加载鸢尾花数据集\n",
    "iris = load_iris()\n",
    "X = iris.data  # 特征数据\n",
    "y = iris.target  # 标签数据\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 归一化数据\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 将数据转换为PyTorch张量并移至GPU\n",
    "X_train = torch.FloatTensor(X_train).to(device)\n",
    "y_train = torch.LongTensor(y_train).to(device)\n",
    "X_test = torch.FloatTensor(X_test).to(device)\n",
    "y_test = torch.LongTensor(y_test).to(device)\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(4, 10)  # 输入层到隐藏层\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(10, 3)  # 隐藏层到输出层\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 实例化模型并移至GPU\n",
    "model = MLP().to(device)\n",
    "\n",
    "# 分类问题使用交叉熵损失函数\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# 使用随机梯度下降优化器\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 20000  # 训练的轮数\n",
    "\n",
    "# 用于存储每200个epoch的损失值和对应的epoch数\n",
    "train_losses = []  # 存储训练集损失\n",
    "test_losses = []   # 存储测试集损失\n",
    "epochs = []\n",
    "\n",
    "# ===== 新增早停相关参数 =====\n",
    "best_test_loss = float('inf')  # 记录最佳测试集损失\n",
    "best_epoch = 0                 # 记录最佳epoch\n",
    "patience = 50                # 早停耐心值（连续多少轮测试集损失未改善时停止训练）\n",
    "counter = 0                    # 早停计数器\n",
    "early_stopped = False          # 是否早停标志\n",
    "# ==========================\n",
    "\n",
    "start_time = time.time()  # 记录开始时间\n",
    "\n",
    "# 创建tqdm进度条\n",
    "with tqdm(total=num_epochs, desc=\"训练进度\", unit=\"epoch\") as pbar:\n",
    "    # 训练模型\n",
    "    for epoch in range(num_epochs):\n",
    "        # 前向传播\n",
    "        outputs = model(X_train)  # 隐式调用forward函数\n",
    "        train_loss = criterion(outputs, y_train)\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        train_loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # 记录损失值并更新进度条\n",
    "        if (epoch + 1) % 200 == 0:\n",
    "            # 计算测试集损失\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                test_outputs = model(X_test)\n",
    "                test_loss = criterion(test_outputs, y_test)\n",
    "            model.train()\n",
    "            \n",
    "            train_losses.append(train_loss.item())\n",
    "            test_losses.append(test_loss.item())\n",
    "            epochs.append(epoch + 1)\n",
    "            \n",
    "            # 更新进度条的描述信息\n",
    "            pbar.set_postfix({'Train Loss': f'{train_loss.item():.4f}', 'Test Loss': f'{test_loss.item():.4f}'})\n",
    "            \n",
    "            # ===== 新增早停逻辑 =====\n",
    "            if test_loss.item() < best_test_loss: # 如果当前测试集损失小于最佳损失\n",
    "                best_test_loss = test_loss.item() # 更新最佳损失\n",
    "                best_epoch = epoch + 1 # 更新最佳epoch\n",
    "                counter = 0 # 重置计数器\n",
    "                # 保存最佳模型\n",
    "                torch.save(model.state_dict(), 'best_model.pth')\n",
    "            else:\n",
    "                counter += 1\n",
    "                if counter >= patience:\n",
    "                    print(f\"早停触发！在第{epoch+1}轮，测试集损失已有{patience}轮未改善。\")\n",
    "                    print(f\"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}\")\n",
    "                    early_stopped = True\n",
    "                    break  # 终止训练循环\n",
    "            # ======================\n",
    "\n",
    "        # 每1000个epoch更新一次进度条\n",
    "        if (epoch + 1) % 1000 == 0:\n",
    "            pbar.update(1000)  # 更新进度条\n",
    "\n",
    "    # 确保进度条达到100%\n",
    "    if pbar.n < num_epochs:\n",
    "        pbar.update(num_epochs - pbar.n)  # 计算剩余的进度并更新\n",
    "\n",
    "time_all = time.time() - start_time  # 计算训练时间\n",
    "print(f'Training time: {time_all:.2f} seconds')\n",
    "\n",
    "# ===== 新增：加载最佳模型用于最终评估 =====\n",
    "if early_stopped:\n",
    "    print(f\"加载第{best_epoch}轮的最佳模型进行最终评估...\")\n",
    "    model.load_state_dict(torch.load('best_model.pth'))\n",
    "# ================================\n",
    "\n",
    "# 可视化损失曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, train_losses, label='Train Loss')\n",
    "plt.plot(epochs, test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss over Epochs')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 在测试集上评估模型\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    correct = (predicted == y_test).sum().item()\n",
    "    accuracy = correct / y_test.size(0)\n",
    "    print(f'测试集准确率: {accuracy * 100:.2f}%')    "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c6e1058d",
   "metadata": {},
   "source": [
    "上述早停策略的具体逻辑如下\n",
    "\n",
    "\n",
    "- 首先初始一个计数器counter。\n",
    "- 每 200 轮训练执行一次判断：比较当前损失与历史最佳损失。\n",
    "  - 若当前损失更低，保存模型参数。\n",
    "  - 若当前损失更高或相等，计数器加 1。\n",
    "    - 若计数器达到最大容许的阈值patience，则停止训练。\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "之所以设置阈值patience，是因为训练过程中存在波动，不能完全停止训练。同时每隔固定的训练轮次都会保存模型参数，下次可以接着这里训练，缩小训练的范围。\n",
    "\n",
    "\n",
    "我这里之所以没有触发早停策略，有以下几个原因：\n",
    "1. 测试集损失在训练中持续下降或震荡，但未出现连续 patience 轮不改善\n",
    "2. patience值过大，需要调小\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359c4410",
   "metadata": {},
   "source": [
    "实际上，在早停策略中，保存 checkpoint（检查点） 是更优选择，因为它不仅保存了模型参数，还记录了训练状态（如优化器参数、轮次、损失值等），一但出现了过拟合，方便后续继续训练。\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DL",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
